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How good are pathogenicity predictors in detecting benign variants?

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  • Abhishek Niroula
  • Mauno Vihinen

Abstract

Computational tools are widely used for interpreting variants detected in sequencing projects. The choice of these tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment. The performance of the methods varies widely in different performance assessments, for example due to the contents and sizes of test datasets. To address this issue, we obtained 63,160 common amino acid substitutions (allele frequency ≥1% and

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  • Abhishek Niroula & Mauno Vihinen, 2019. "How good are pathogenicity predictors in detecting benign variants?," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-17, February.
    • Handle: RePEc:plo:pcbi00:1006481
    DOI: 10.1371/journal.pcbi.1006481
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    References listed on IDEAS

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    1. Mauno Vihinen, 2015. "No more hidden solutions in bioinformatics," Nature, Nature, vol. 521(7552), pages 261-261, May.
    2. Jaroslav Bendl & Jan Stourac & Ondrej Salanda & Antonin Pavelka & Eric D Wieben & Jaroslav Zendulka & Jan Brezovsky & Jiri Damborsky, 2014. "PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-11, January.
    3. Wenqing Fu & Timothy D. O’Connor & Goo Jun & Hyun Min Kang & Goncalo Abecasis & Suzanne M. Leal & Stacey Gabriel & Mark J. Rieder & David Altshuler & Jay Shendure & Deborah A. Nickerson & Michael J. B, 2013. "Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants," Nature, Nature, vol. 493(7431), pages 216-220, January.
    4. Ilia Korvigo & Andrey Afanasyev & Nikolay Romashchenko & Mikhail Skoblov, 2018. "Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    5. Abhishek Niroula & Siddhaling Urolagin & Mauno Vihinen, 2015. "PON-P2: Prediction Method for Fast and Reliable Identification of Harmful Variants," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
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    Cited by:

    1. Salvatore Daniele Bianco & Luca Parca & Francesco Petrizzelli & Tommaso Biagini & Agnese Giovannetti & Niccolò Liorni & Alessandro Napoli & Massimo Carella & Vincent Procaccio & Marie T. Lott & Shipin, 2023. "APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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